It is well known in the wafer inspection art that when two similar images are to be compared, sub-pixel alignment is often necessary to obtain the degree of accuracy that is desired. Traditionally that alignment was accomplished by digitally interpolating the image after scanning.
The most frequently used method for automatic inspection of photomasks or patterned semiconductor wafers utilizes comparison to detect defects. Typically, two supposedly identical patterns are compared by scanning and digitizing the images. The digitized images are then compared in high speed digital logic, or an image is compared with data stored in the CADS (Computer Aided Design System) database with data representing the desired pattern.
In the comparison process to detect differences between the two patterns some form of image subtraction is most frequently employed. However, image subtraction is contingent on sampling the two images (or the image and image data from the database) at nearly identical points for both images.
Early mask inspection systems, such as taught by Levy, et al., in U.S. Pat. No. 4,247,203, were able to guarantee only a .+-.21/2 pixel registration accuracy between the two images. Because of the limited registration accuracy, Levy required that the defect detection algorithm use feature extraction, followed by the matching of these features, rather than image subtraction. Some time later Levy, U.S. Pat. No. 4,579,455, taught area subtraction, but because of the limited registration accuracy computed the intensity difference at several possible registrations. If, for any of these registrations the absolute value of the intensities was less than a predetermined threshold, no defect was recorded at that particular pixel. Subsequently, Specht, et al., in U.S. Pat. No. 4,805,123, taught a method of achieving image subtraction by first reducing the registration error between the two images to less than a pixel. However, the Specht method had the shortcoming that in re-registering (also known as resampling) the two images with respect to each other, interpolation of the scanned image was used, which in turn introduced errors in determining the intensities of the resulting pixels. These errors limited sensitivity (the smallest detectable defect).
As will be shown subsequently, the maximum intensity error determines the maximum detectable defect-to-pixel ratio. Since inspection speed, at a given sensitivity, defines the productivity of an inspection system, for a fixed sampling rate, it is desirable to maximize the pixel size. Therefore, to achieve the maximum throughput, one must minimize the registration error. The present invention teaches methods for minimizing the registration error for the two most common scanning methods: scanning with a laser and scanning with a linear array.